In statistical meta-learning problems, one line of research is learning of the parameters of the optimization algorithms, and analyzing gradients based on meta-learning methods [1]}, [2]}. For example, [3]}, [4]} worked on an online convex optimization framework with the assumption that tasks are close to a global task parameter. Additionally, [5]}, [6]} studied algorithms which incrementally update the bias regularization parameter using a sequence of observed tasks.
Another line of research is studying the meta-generalization gap, and finding bounds on it on average [7]}, [8]}
or with high probability [9]}, [10]}, [11]}, [12]}, [13]}.
